Blood flow simulation boundary condition calculation method with second-order precision

Abstract

The invention is applicable to the technical field of computational fluid mechanics, and provides a blood flow simulation boundary condition calculation method with second-order precision, according to the method, a second-order implicit difference scheme is used as a specific numerical method to discretize a ternary Windkessel equation, and compared with a backward Euler method used in previous research, the method has higher calculation precision, and the calculation efficiency is improved. And the influence on the calculation efficiency is very small. The storage medium for storing the method and the processor for operating the method also have the same technical effects.

Description

Technical Field

[0001] The present application belongs to the field of computational fluid dynamics technology, and in particular, relates to a blood flow simulation boundary condition calculation method with second-order accuracy, a storage medium, and a processor. Background Art

[0002] In the prior art, when blood flow simulation based on computational fluid dynamics (CFD) is used to assess the risk of abdominal aortic aneurysm rupture, due to errors in the data acquisition process, limitations of the reconstruction algorithm, and the influence of the object's own characteristics, these factors may cause the accuracy of the model and simulation results to decrease, affecting the accuracy of the simulation results. In particular, when the backward Euler format is used to discretize the ternary Windkessel equation, since its format has only first-order accuracy, the error of the calculated analytical solution can only be guaranteed to be less than 5%, and the error range is relatively large. The prior art has shortcomings. Summary of the invention

[0003] The purpose of the present application is to provide a blood flow simulation boundary condition calculation method, storage medium and processor with second-order accuracy, aiming to solve the technical problem of insufficient calculation accuracy when using the backward Euler method used in the prior art.

[0004] On the one hand, the present application provides a method for calculating boundary conditions for blood flow simulation with second-order accuracy, comprising performing three-dimensional reconstruction of the abdominal aorta through medical tomography imaging data; then meshing the reconstructed model to discretize the continuous intravascular environment into a finite number of units; the method also includes the following steps:

[0005] s1. When performing fluid dynamics simulation, a second-order implicit difference format is used to discretize the three-variable Windkessel equations;

[0006] s2. Solving the discretized ternary Windkessel equation to obtain the discretized inlet and outlet boundary conditions of the unit to simulate the blood flow environment in a real blood vessel;

[0007] s3. Based on the calculation results of fluid dynamics simulation, Paravierw is used to calculate the hemodynamic parameters of the model, including: blood flow velocity, pressure, shear stress, shear oscillation index and relative residence time.

[0008] Furthermore, in step s2, the output of one cardiac cycle is used as the inlet boundary condition, and the output and pressure changes within one cardiac cycle are used to obtain the initial conditions and various calculation parameters for solving the ternary Windkessel equation.

[0009] Furthermore, in step s2, for the unit with a complex geometric structure having multiple outlets, the boundary condition of the ternary Windkessel equation is applied to each outlet, and the simulated pressure is at a normal physiological value for humans by adjusting the values ​​of total resistance and total compliance.

[0010] Furthermore, in step s2, when using ANSYS CFX to implement the Windkessel model, four additional variables are defined to temporarily save the pressure and flow of each outlet in the previous time step, and the pressure and flow of each outlet in the previous two time steps, so that these four data can be passed to the next time step in ANSYS CFX.

[0011] Furthermore, in step s3, each hemodynamic parameter of Paravierw is calculated using a numerical integration method.

[0012] Furthermore, the medical tomography imaging data includes normal abdominal aorta data and abdominal aorta data with aneurysm.

[0013] Furthermore, in the process of three-dimensional reconstruction of the abdominal aorta, real-time processing and multimodal fusion are used to achieve rapid processing and integration of multiple imaging data.

[0014] Furthermore, during the three-dimensional reconstruction of the abdominal aorta, advanced image segmentation algorithms were used to ensure clear model boundaries, and the model was post-processed to remove noise and increase details.

[0015] Furthermore, during the three-dimensional reconstruction of the abdominal aorta, the accuracy of the reconstruction was verified by comparing the simulation with the solid model.

[0016] Furthermore, during the three-dimensional reconstruction of the abdominal aorta, the model is regularly updated in combination with the latest clinical data to improve the accuracy and reliability of the model.

[0017] On the other hand, the present application also provides a storage medium storing a program file capable of implementing the above-mentioned blood flow simulation boundary condition calculation method with second-order accuracy.

[0018] On the other hand, the present application also provides a processor, which is used to run a program, wherein the program executes the above-mentioned blood flow simulation boundary condition calculation method with second-order accuracy when running.

[0019] This application uses a specific numerical method (second-order implicit difference format) to discretize the ternary Windkessel equations. This method has higher calculation accuracy than the backward Euler method used in too much research, and has little impact on calculation efficiency. At the same time, this application uses the output of a cardiac cycle as the inlet boundary condition, and uses the output and pressure changes within a cardiac cycle to obtain the initial conditions for solving the ternary Windkessel equations and the calculation of various parameters, which also helps to improve the final closeness to the analytical solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a schematic diagram of the process of blood flow simulation using the present application;

[0021] Figure 2 It is the schematic diagram of the ternary Windkessel boundary condition of this application;

[0022] Figure 3 This is a comparison chart of the experimental model and parameter curve provided in Example 4 of the present application;

[0023] Figure 4 is a schematic diagram of global mesh division of the three-dimensional model of the abdominal aorta adopted in this application;

[0024] Figure 5 This is the main flow chart of the blood flow simulation boundary condition calculation method with second-order accuracy provided in Example 1 of the present application. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0026] The specific implementation of this application is described in detail below in conjunction with specific embodiments:

[0027] Embodiment 1:

[0028] Figures 1 to 5 The main process of implementing the blood flow simulation boundary condition calculation method with second-order accuracy provided in the first embodiment of the present application is shown. For the sake of convenience, only the part related to the embodiment of the present application is shown, which is described in detail as follows:

[0029] On the one hand, if Figure 5 As shown, the present application provides a method for calculating blood flow simulation boundary conditions with second-order accuracy, including: Figure 4The abdominal aorta is reconstructed in three dimensions using medical tomography imaging data as shown; the reconstructed model is then meshed to discretize the continuous intravascular environment into a finite number of units; the method also includes: Figure 5 The following steps are shown:

[0030] s1. When performing fluid dynamics simulation, a second-order implicit difference format is used to discretize the ternary Windkessel equations; based on the second-order implicit difference format with second-order accuracy, the accuracy of blood flow simulation is improved.

[0031] s2. Solving the discretized ternary Windkessel equation to obtain the discretized inlet and outlet boundary conditions of the unit to simulate the blood flow environment in a real blood vessel;

[0032] s3. Based on the calculation results of fluid dynamics simulation, Paraview was used to calculate the hemodynamic parameters of the model, including blood flow velocity, pressure, shear stress, shear oscillation index and relative residence time.

[0033] In specific implementation, using Paraview for post-processing has the following advantages:

[0034] A. Paraview has a variety of visualization technologies and provides a variety of visualization methods, such as isosurfaces, streamlines, particle tracking, etc., which helps this application to intuitively observe the spatial distribution of various parameters.

[0035] B. Paraview has powerful data analysis capabilities. In addition to visualization, Paraview also provides a wealth of data analysis tools, such as statistical analysis, data filtering, data extraction, etc., to help this application calculate hemodynamic parameters.

[0036] C. Paraview can export visualization results into multiple formats (such as pictures, animations, data files, etc.) to facilitate the reporting and sharing of the results of this application.

[0037] Attached Figure 1 The complete process of the present application from data acquisition to three-dimensional modeling and then to specific blood flow simulation is shown. The numerical calculation part adopts the second-order implicit difference format, which effectively improves the accuracy of blood flow simulation.

[0038] As attached Figure 2 The schematic diagram of the three-element Windkessel boundary condition is shown in Figure 1. Ci represents the total compliance of each outlet. represents the proximal resistance of each outlet, It represents the remote resistance of each outlet, Qi represents the flow rate of each outlet, and Pi represents the pressure of each outlet.

[0039] This application uses the second-order implicit difference format to replace the backward Euler format used in previous studies. Because the second-order implicit difference format can provide second-order accuracy. The detailed proof process of second-order accuracy is as follows:

[0040] Consider the differential equation: Here y represents all second-order differentiable functions, corresponding to the two derivative terms of the ternary Windkessel equation (the flow rate of each outlet represented by Qi and the pressure of each outlet represented by Pi).

[0041] The formula for the second-order implicit difference format is defined as: n represents the current time step.

[0042] Prove that for y n and n-2 Perform Taylor expansion:

[0043]

[0044] Substituting into the formula of the second-order implicit difference format, we get:

[0045]

[0046] After simplification, we can get:

[0047]

[0048] Taylor expansion on the right side of the equation yields:

[0049]

[0050] Simplifying, we get:

[0051]

[0052] Among them, t represents time, which is used to simulate the blood flow time in a cardiac cycle; Δt represents the time step, O(Δt 2 ) and O(Δt 3 ) represent second-order infinitesimals and third-order infinitesimals respectively, and denote the first and second derivatives of y with respect to t, respectively. and They represent the partial derivatives of f with respect to y and the partial derivatives of f with respect to t respectively.

[0053] Therefore, the error E n =|y n -y(t n )|~O(Δt 2 ), represents the error E n and the second-order infinitesimal O(Δt 2), so the error produced by the second-order implicit difference format is second-order in each step. Therefore, it can be concluded that the second-order implicit difference format is second-order accurate.

[0054] It can be seen that compared with the backward Euler format used in the prior art which has only first-order accuracy, discretizing the ternary Windkessel equations with a second-order implicit difference format can provide higher calculation accuracy.

[0055] In specific implementation, the ternary Windkessel equation is:

[0056]

[0057] Among them, Figure 2 As shown, at the exit of the blood vessel, C i represents the total compliance of each outlet, represents the proximal resistance of each outlet, represents the far-end resistance of each outlet, Q i represents the flow rate of each outlet, P i Indicates the pressure at each outlet.

[0058] After discretization, we can get:

[0059]

[0060] Among them, P n and Q n Respectively represent the pressure and flow rate of each outlet in the current time step, P n-1 and Q n-1 Respectively represent the pressure and flow rate of each outlet in the previous time step, P n-2 and Q n-2 Respectively represent the pressure and flow rate of each outlet in the first two time steps, and P n As pressure boundary condition at the outlet.

[0061] The calculation of the parameters of the ternary Windkessel equation is expressed as:

[0062] According to the average inlet pressure P m , average flow Q m and the surface area S of each outlet i (represents the surface area of ​​the ith outlet) to calculate the resistance and compliance parameters of the ith outlet. This completes the boundary condition calculation of all blood vessel outlets.

[0063] The detailed calculation formula is as follows:

[0064]

[0065]

[0066] The above two formulas show how the parameters of “each” outlet are calculated based on the “total” parameters and the area of ​​“each” outlet;

[0067]

[0068] Among them, R total Represents the total resistance, C total represents the total compliance, M represents the number of outlets, τ represents the pressure drop constant, and τ = 1.79 seconds.

[0069] Furthermore, in step s2, the flow rate of a cardiac cycle is used as the inlet boundary condition, and the initial flow rate and the average flow rate Q in a cardiac cycle are used. m and initial pressure and average pressure P m The initial conditions and various calculation parameters for solving the ternary Windkessel equation are obtained.

[0070] Furthermore, in step s2, for the unit with a complex geometric structure having multiple outlets, the boundary conditions of the ternary Windkessel equation are applied to each outlet to simulate the actual pulsating pressure, and the values ​​of total resistance and total compliance are adjusted so that the range of blood flow simulation pressure is within the normal physiological value of a human or matches the clinical measurement value, thereby making the blood flow simulation closer to the actual situation and the conclusion more convincing.

[0071] Furthermore, in step s2, when using ANSYS CFX to implement the Windkessel model, four additional variables are defined to temporarily save the pressure and flow of each outlet in the previous time step, and the pressure and flow of each outlet in the previous two time steps, so that these four data can be passed to the next time step in ANSYS CFX.

[0072] Specifically, when implementing the Windkessel model in ANSYS CFX, due to the P n-1 (pressure at each outlet in the previous time step), Q n-1 (the flow rate of each outlet in the previous time step), P n-2 (pressure at each outlet in the first two time steps) and Q n-2 (The flow rate of each outlet in the first two time steps) is not directly available in ANSYS CFX. This application defines four additional variables to temporarily save the pressure and flow rate of each outlet in the previous and first two time steps, and pass them to the next time step.

[0073] At the same time, in CFX, add the code "Update Loop = Trans_loop" in the command editor of the additional variables to read the pressure and flow values ​​of the previous time step and the previous two time steps.

[0074] In other preferred embodiments, the command language and expression language in ANSYS CFX are used to calculate the flow rate of the current time step, and the discretized ternary Windkessel equation can also be obtained through the expression.

[0075] Furthermore, in step s3, each hemodynamic parameter of Paravierw is calculated using the surface integral method using numerical integration. In hemodynamics, the surface integral usually represents the sum or average of blood flow, pressure or other physiological parameters in a specific area (such as a blood vessel or a heart chamber). This integral can be used to calculate flow, blood flow velocity or other dynamic characteristics. The core of numerical integration is to simplify complex mathematical models into a computable form. By discretizing continuous functions, these hemodynamic phenomena can be simulated on a computer. This method is particularly useful when dealing with nonlinear and complex equations, which are usually difficult to find analytical solutions. In biological hemodynamics, blood vessels and heart chambers usually have complex geometric structures. Numerical integration methods can adapt to these complex shapes, making the analysis of flow fields more realistic.

[0076] Furthermore, in the process of three-dimensional reconstruction of the abdominal aorta, real-time processing and multimodal fusion are used to achieve rapid processing and integration of multiple imaging data. Real-time processing means that when imaging data is collected, the data can be processed immediately so as to obtain results quickly.

[0077] For the 3D reconstruction of the abdominal aorta, this process can be achieved by the following methods: Efficient algorithms: Use fast image processing algorithms (such as fast Fourier transform, fast edge detection algorithm, etc.) that can process a large amount of image data in a short time. Parallel computing: Use multi-core processors and graphics processing units (GPUs) for parallel computing, which can significantly speed up data processing. For example, GPU acceleration for image segmentation and reconstruction can process multiple image frames at the same time. Real-time image stream processing: Using streaming processing technology, data can be received and processed frame by frame and fragment by fragment, avoiding waiting for the entire data set to load. This can be applied to imaging technologies such as ultrasound, CT or MRI to analyze and dynamically monitor changes in the abdominal aorta in real time. Multimodal fusion refers to the integration of data from different imaging modalities to build a more comprehensive 3D model of the abdominal aorta. Fusion methods usually include: Image registration: Before multimodal fusion, data from different imaging modalities need to be registered to ensure that the same anatomical structure can be aligned on different images. This can be achieved through feature matching, rigid or non-rigid transformation algorithms. Data fusion algorithm: Use fusion algorithms, such as weighted fusion, principal component analysis, or wavelet transform, to integrate data from different imaging sources. For example, when fusing CT and MRI data, the high resolution of CT and the soft tissue contrast advantage of MRI can be used to obtain comprehensive information. Deep learning model: Use deep learning technology for image fusion. By training the convolutional neural network model, data from different modalities can be better extracted and fused to improve the quality and accuracy of the final 3D reconstruction. Information enhancement: During the fusion process, it is important to pay attention to the retention of important anatomical details to ensure the clarity and effectiveness of the final model.

[0078] Furthermore, in the process of 3D reconstruction of the abdominal aorta, the advanced image segmentation algorithm is used to ensure the clear boundaries of the model, and the model is post-processed to remove noise and increase details. The advanced image segmentation algorithm shows its superiority in many aspects, mainly in the following aspects:

[0079] Deep learning technology: In recent years, deep learning methods such as convolutional neural networks have shown extremely high accuracy in image segmentation tasks. Multi-scale feature extraction: Advanced algorithms usually use multi-scale feature extraction technology, which can effectively capture large-scale and small-scale structural information, which is very important for processing target objects with different sizes and shapes.

[0080] Fully automatic segmentation: Modern algorithms can achieve fully automatic segmentation without human intervention or prior knowledge, making the application more extensive and efficient. Especially in medical image processing, it can reduce the workload of doctors and provide efficient auxiliary tools.

[0081] Adaptive learning: Segmentation algorithms based on deep learning can gradually adapt to different image contents and types by training on a large amount of labeled data, thereby improving the versatility and adaptability of the algorithm.

[0082] Use of large-scale datasets: Some algorithms can effectively use large datasets for training, improve the generalization ability of the model, and enable it to perform better in practical applications. Post-processing and optimization: Many algorithms include post-processing steps to enhance the coherence and naturalness of the segmentation results and reduce noise and artifacts through smoothing and optimization algorithms.

[0083] Furthermore, during the three-dimensional reconstruction of the abdominal aorta, the accuracy of the reconstruction is verified by comparing the simulation with the physical model. The following are the specific comparison steps and how to reflect the accuracy verification process:

[0084] 1. Physical model construction: A physical model is made based on medical imaging data. Usually, 3D printing or other manufacturing technologies can be used to generate a realistic abdominal aorta model.

[0085] 2. Realization of 3D reconstruction, computer generated 3D model, image preprocessing: denoising, filtering and enhancement of the collected image data. Segmentation: Use advanced image segmentation algorithms to separate the abdominal aorta from surrounding structures. Reconstruction: Use the data obtained by segmentation to use 3D reconstruction algorithms (such as voxel method, surface reconstruction, etc.) to generate a 3D model of the abdominal aorta.

[0086] 3. Compare the simulation with the physical model, set the comparison standard simultaneously, and standardize the comparison points: determine the comparison indicators, such as diameter, length, curvature, volume and structural details. These indicators can directly measure the corresponding parts in the simulation model and the physical model. Alignment and registration: Ensure the alignment of the two models, usually through feature point matching, rigid transformation and other methods. This step is crucial to ensure the accuracy of the comparison. Dimension measurement: Use three-dimensional space measurement tools to measure the model and compare the differences in multiple dimensions such as size, shape and volume between the simulation model and the physical model.

[0087] 4. Accuracy verification, analysis of deviations: Compare the differences between the physical model and the simulation model in various measurement indicators, calculate their percentage errors, and analyze their rationality. Verify clinical importance: Combined with clinical needs, evaluate whether the differences in the simulation model are within the medically acceptable range. For example, for preoperative evaluation, the tolerance of some parameters may be smaller. Feedback implementation: Based on the comparison results, evaluate the accuracy of the reconstruction algorithm and model construction, and optimize and adjust the parameters if necessary.

[0088] like Figure 4 As shown, part A is the global mesh division of the 3D model of the abdominal aorta, Γ IIndicates the entrance. represents the exit, Γ W Represents the blood vessel wall. Parts B and D are enlarged views of the local mesh and volume mesh, respectively. Parts C and E are enlarged views of the surface mesh and boundary layer mesh of the outlet, respectively.

[0089] This application uses a specific numerical method (second-order implicit difference format) to discretize the ternary Windkessel equations. This method has higher calculation accuracy than the backward Euler method used in previous studies, and has little impact on calculation efficiency. At the same time, this application uses the output of a cardiac cycle as the inlet boundary condition, and uses the output and pressure changes within a cardiac cycle to obtain the initial conditions for solving the ternary Windkessel equations and the calculation of various parameters, which also helps to improve the final closeness to the analytical solution.

[0090] Furthermore, the present application uses the ternary Windkessel model parameters to simulate blood flow conditions in blood vessels, including blood flow velocity, blood pressure changes, etc. The output based on the cardiac cycle is used as the inlet boundary condition, so that the change and size of the outlet pressure in a cardiac cycle can be more consistent with the actual situation.

[0091] Embodiment 2:

[0092] On the other hand, the present application also provides a storage medium storing a program file capable of implementing the above-mentioned blood flow simulation boundary condition calculation method with second-order accuracy.

[0093] A person skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, such as ROM / RAM, disk, CD-ROM, etc.

[0094] Embodiment three:

[0095] On the other hand, the present application also provides a processor, which is used to run a program, wherein the program executes the above-mentioned blood flow simulation boundary condition calculation method with second-order accuracy when running.

[0096] In the embodiment of the present application, the blood flow simulation boundary condition calculation method with second-order accuracy can be implemented by corresponding hardware or software units, and each unit can be an independent software or hardware unit, or can be integrated into a software or hardware unit, which is not intended to limit the present application. The specific implementation of each unit can refer to the description of embodiment 1, which will not be repeated here.

[0097] Embodiment 4:

[0098] In the specific implementation process, the blood flow simulation boundary condition calculation method with second-order accuracy in this application solves the technical problem of insufficient first-order accuracy. The following is a detailed description through a specific comparative verification process.

[0099] As attached Figure 3 As shown, in order to verify the effectiveness of the blood flow simulation boundary condition calculation method with second-order accuracy proposed in this application, the effectiveness of the proposed method is verified by combining several groups of comparative simulation results.

[0100] Attached Figure 3 In the figure, part A is: the three-dimensional geometric model and detailed parameters of the experiment, part B is: the inlet boundary conditions of the experiment, parts C and D are: the experimental results are inlet pressure and outlet pressure respectively, Analytical represents the analytical solution, BDF2 represents the result of the second-order implicit difference format, Euler represents the result of the backward Euler method, and the enlarged views in C and D show that: compared with the backward Euler method, the results of the second-order implicit difference format are closer to the analytical solution.

[0101] In the simulation experiment, this application simulated a blood vessel with a length of 200 mm and a diameter of 20 mm, and the blood density was set to 1056 kg / m 3 , the viscosity is set to 0.0035 Pa·s, the inlet boundary condition is the mass flow of one cardiac cycle, the outlet boundary condition is the ternary Windkessel boundary condition, and the wall boundary condition is no slip, rigid wall, and the velocity is 0.

[0102] The simulation results of the present invention are compared with the analytical solutions of previously published literature, and the results show that both the overall and local distributions conform to the corresponding laws, and the corresponding values ​​are also within the reference range of the existing literature.

[0103] The present invention uses two different discrete methods (backward Euler format and second-order implicit difference format) for simulation, and compares and calculates the errors with the analytical solutions in previous published literature. The results show (as shown in the following table): the errors are all less than 5%, and the second-order implicit difference format with second-order accuracy used in the present invention has a smaller error, all less than 1.5%, which is closer to the analytical solution.

[0104] Calculation results of relative error:

[0105]

[0106] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for calculating boundary conditions for blood flow simulation with second-order accuracy, comprising three-dimensionally reconstructing the abdominal aorta using medical tomography imaging data; then meshing the reconstructed model to discretize the continuous intravascular environment into a finite number of units; characterized in that, The method further comprises the steps of: s1. When performing fluid dynamics simulation, a second-order implicit difference format is used to discretize the three-variable Windkessel equations; s2. Solving the discretized ternary Windkessel equation to obtain the discretized inlet and outlet boundary conditions of the unit to simulate the blood flow environment in a real blood vessel; s3. Based on the calculation results of fluid dynamics simulation, Paravierw is used to calculate the hemodynamic parameters of the model, including: blood flow velocity, pressure, shear stress, shear oscillation index and relative residence time.

2. The method according to claim 1, characterized in that In step s2, the output of one cardiac cycle is used as the inlet boundary condition, and the output and pressure changes within one cardiac cycle are used to obtain the initial conditions and various calculation parameters for solving the ternary Windkessel equation.

3. The method according to claim 2, characterized in that In step s2, for the unit with a complex geometric structure having multiple outlets, the boundary condition of the three-variable Windkessel equation is applied to each outlet, and the simulated pressure is adjusted to the normal physiological value of a human by adjusting the values ​​of the total resistance and the total compliance.

4. The method according to claim 3, characterized in that In step s2, when using ANSYS CFX to implement the Windkessel model, four additional variables are defined to temporarily save the pressure and flow of each outlet in the previous time step, and the pressure and flow of each outlet in the previous two time steps, so that these four data can be passed to the next time step in ANSYS CFX.

5. The method according to claim 1, characterized in that In step s3, each hemodynamic parameter of Paravierw is calculated using a numerical integration method.

6. The method according to claim 1, characterized in that The medical tomography imaging data includes normal abdominal aorta data and abdominal aorta data with aneurysm.

7. The method according to claim 1, characterized in that In the process of three-dimensional reconstruction of the abdominal aorta, real-time processing and multimodal fusion are used to achieve rapid processing and integration of multiple imaging data.

8. The method according to claim 7, characterized in that During the three-dimensional reconstruction of the abdominal aorta, advanced image segmentation algorithms are used to ensure clear model boundaries, and the model is post-processed to remove noise and increase details.

9. The method according to claim 8, characterized in that During the three-dimensional reconstruction of the abdominal aorta, the accuracy of the reconstruction was verified by comparing the simulation with the solid model.

10. The method according to claim 9, characterized in that During the three-dimensional reconstruction of the abdominal aorta, the model is regularly updated in combination with the latest clinical data to improve its accuracy and reliability.

11. A storage medium, characterized in that: The storage medium stores a program file capable of implementing the blood flow simulation boundary condition calculation method with second-order accuracy as described in any one of claims 1 to 10.

12. A processor, characterized in that: The processor is used to run a program, wherein the program, when running, executes the blood flow simulation boundary condition calculation method with second-order accuracy as described in any one of claims 1 to 10.

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